pyGIMLi

An open-source for Geophysical Modeling and Inversion in research and teaching

Carsten Rücker
carsten@pygimli.org

TU Berlin, Germany

Thomas Günther
thomas@pygimli.org

LIAG, Hannover, Germany

Florian Wagner
florian@pygimli.org

RWTH Aachen University, Germany

2024-03-19

pyGIMLi is a versatile open-source toolbox with:

  • management tools for structured and unstructured meshes in 2D & 3D
  • computationally efficient finite-element and finite-volume solvers
  • various geophysical forward operators: ERT/IP, Traveltime, Gravimetry, Magnetics, SP, EM
  • frameworks for constrained, joint and process-based inversions with region-specific regularization
  • open-source, platform compatible, documented & tested code
  • suitability for teaching & reproducible research
  • 1.0 version published in 2017 in Computers and Geosciences (Rücker, Günther, and Wagner 2017) (and among the Most Downloaded papers since, > 330 citations, > 100 uses in peer-reviewed papers)

pyGIMLi aims to make:

  • simple things easy
  • hard things possible
  • workflows transparent and reproducible
  • your choice of everydays tools
  • people building their own projects upon
    • pyBERT, REDA, COMET, custEM, SAEM, PyRefract

It is rather a toolbox with lots of toolsl to combine than a ready-purpose code

Existing tutorials

  • Transform 2021: creating geometries & meshes, modeling PDEs, synthetic data creation, inversion (also with external forward operators).
  • Transform 2022: fundamental pyGIMLi objects (Mesh, DataContainer, matrix types, etc.), geostatistical vs. smoothness regularization, treatment of subsurface regions, adding prior data.
  • Today we will show you how to invert a real-life 3D data set (Hübner et al. 2017) with many ways to tweak your inversion beyond the standard practice.

What is new since?

  • Transform ’21 & ’22 notebooks available as tutorials or examples on pygimli.org
  • Improved 3D visualization powered by pyvista (including filters, slices and interactive notebook compatibility)
  • 3D gravity and (full-tensor) magnetics operators and managers
  • New matrices and matrix generators, e.g. non-explicit (PDE-based) Jacobian matrix
  • LSQR inversion framework allowing parameter constraints (from Wagner et al. 2019)
  • Multi-frame modelling framework MultiFrameModelling for temporally/spectrally/spatially constrained inversion
  • TimelapseERT class with different strategies, e.g. 4D inversion
  • New examples on ERT (2D/3D crosshole, 3D surface, timelapse), IP, 3D magnetics
  • Improved website, i.e. fully upgraded to modern (pg>1.2) style and moved to the pydata-sphinx-theme
  • Many more convenience functions to simplify the code
  • Many new papers using pyGIMLi (https://pygimli.org/publist.html)

Join the pyGIMLi user community!

“In open source, we feel strongly that to really do something well, you have to get a lot of people involved.”

– Linus Torvalds

  1. Join the #pyGIMLi chat on Mattermost!
  2. Open a discussion or **raise an issue”“” on GitHub.
  3. Contribute to the website via the “Improve this page” button in the right sidebar.
  4. Add your pyGIMLi-powered publication to this database.
  5. Send your example to mail@pygimli.org.
  6. Contribute to the code as described in our contribution guidelines.

How to get started

  1. Open the Anaconda Prompt () or a Terminal (/).
  2. Clone the SEGwebinar repository.
git clone https://github.com/gimli-org/SEGwebinar.git
cd SEGwebinar
  1. Install the pg environment with the required dependencies (in particular pygimli=1.5.0).
conda env create
  1. Activate the environment and start a Jupyter Notebook.
conda activate pg
jupyter notebook

Follow without a local installation

You can also visit https://colab.research.google.com, open an empty notebook and type !pip install pygimli.

Let’s go!

  1. A single story with a timelapse ERT data

  2. load, process and visualize data

  3. work on different meshes

  4. use different regularization approaches

  5. add prior information and petrophysics

  6. Visualize results in 2D and 3D

Source: Hübner et al. (2017)
Hübner, R., T. Günther, K. Heller, U. Noell, and A. Kleber. 2017. “Impacts of a Capillary Barrier on Infiltration and Subsurface Stormflow in Layered Slope Deposits Monitored with 3-d ERT and Hydrometric Measurements.” Hydrology and Earth System Sciences 21 (10): 5181–99. https://doi.org/10.5194/hess-21-5181-2017.
Rücker, C., T. Günther, and F. M. Wagner. 2017. “pyGIMLi: An Open-Source Library for Modelling and Inversion in Geophysics.” Computers and Geosciences 109: 106–23. https://doi.org/10.1016/j.cageo.2017.07.011.
Wagner, F. M., C. Mollaret, T. Günther, A. Kemna, and C. Hauck. 2019. “Quantitative Imaging of Water, Ice, and Air in Permafrost Systems Through Petrophysical Joint Inversion of Seismic Refraction and Electrical Resistivity Data.” Geophysical Journal International 219 (3): 1866–75. https://doi.org/10.1093/gji/ggz402.

https://seg24.pygimli.org